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The Hacker's Guide to Scaling Python

Learn about Python's scalability, concurrency, and distribution via CPU scaling, event loops, queue-based distribution, and building REST APIs to optimize and deploy high-performance applications.

4.5

99 Lessons

2 Projects

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Learning Roadmap

99 Lessons1 Project13 Quizzes1 Challenge

Scaling

Step through scaling Python applications using multithreading, distributing systems, and service-oriented architecture.

Introduction to Scaling

Scaling Across CPUs

Distributed Systems

Service-Oriented Architecture

Quiz on Scaling

CPU Scaling

Walk through CPU scaling techniques using threads, processes, futures, and optimization tools.

Introduction to CPU Scaling

Using Threads

Using Processes

Using Futures

Advanced Futures Usage

Daemon Processes

Mehdi Abaakouk on CPU Scaling

Quiz on Scaling Across CPUs

Coding Challenge: Scaling Across CPUs

Event Loops

7 Lessons

Work your way through event loops, asynchronous code, and efficient network management using asyncio.

Functional Programming

5 Lessons

Grasp the fundamentals of functional programming in Python, its toolkit, and practical applications.

Queue-Based Distribution

10 Lessons

Dig into Python's queue-based distribution, focusing on RQ and Celery for scalability.

Designing for Failure

5 Lessons

Tackle exception handling, retry strategies, and Tenacity for robust Python applications.

Create a Web Crawler

Project

Project Walkthrough

Practice using Celery and Redis to efficiently manage and process tasks.

Project Solution Review

Lock Management

9 Lessons

Step through managing locks to ensure safe access and prevent data corruption.

Group Membership

9 Lessons

Unpack the core of managing group membership in distributed systems using the Tooz library.

10.

REST Interfaces

11 Lessons

Master the steps to build scalable and efficient REST APIs with Python frameworks.

Build a REST API in Flask

Project

Premium

11.

Deploying on PaaS

7 Lessons

Break down the steps to deploying Python applications on various PaaS platforms.

12.

Testing Distributed Systems

5 Lessons

Map out the steps for testing, environment setup, and service management in distributed systems.

13.

Caching

7 Lessons

Investigate caching to enhance Python app performance, covering local, memoization, and distributed methods.

14.

Performance

8 Lessons

Master the steps to optimize Python performance through profiling, disassembling code, and efficient memory use.

15.

Conclusion

Engage in practical projects to apply your scalable Python skills confidently.

Conclusive Remarks

Certificate of Completion

Showcase your accomplishment by sharing your certificate of completion.

Developed by MAANG Engineers

ABOUT THIS COURSE

Python, in all of its greatness, is often dismissed when needing to write performant and distributed applications. It’s considered to be slow and not suited for the task. In this course, you will find that with the right implementation of Python, you can write applications that scale horizontally, perform well, and are distributed. To kick things off, you’ll learn about CPU scaling, concurrency, and event loops, all of which are crucial for implementing a distributed system. After that, you’ll move on to queue-based distribution, lock management, and group memberships, which are fundamental concepts for maintaining availability and consistency in distributed applications. In the last half, you’ll learn how to build a REST API, how to deploy your app to a PaaS, and how to optimize your application's performance. By the end of this course, you’ll be more productive with Python, you’ll be able to write faster applications that are distributed, and you’ll have a great new skill that is highly coveted.

ABOUT THE AUTHOR

Julien Danjou

Open Source hacker for the last 20 years, I contribute and maintain a lot of Python code and libraries.

Learn more about Julien

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